Halide-Induced Step Faceting and Dissolution Energetics from Atomistic Machine Learned Potentials on Cu(100)

Adsorbates impact the surface stability and reactivity of metallic electrodes, affecting the corrosion, dissolution, and deposition behavior. Here, we use density functional theory (DFT) and DFT-based Behler–Parrinello neural networks (BPNN) to investigate the geometry, surface formation energy, and atom removal energy of stepped and kinked surfaces vicinal to Cu(100) with a c(2 × 2) Cl adlayer. DFT calculations indicate that the stable structures for the adsorbate-free vicinal surfaces favor steps with ⟨110⟩ orientation, while the addition of a c(2 × 2) Cl adlayer leads to ⟨100⟩ step faceting, in agreement with scanning tunneling microscopy (STM) observations. The BPNN calculations produce energies in good agreement with DFT results (root-mean-square error of 1.3 meV/atom for a randomly chosen set of structures excluded from the training set). We draw three conclusions from the BPNN calculations. First, Cl on the upper ⟨100⟩ step edges occupies the 3-fold hollow sites (as opposed to the 4-fold sites on the terraces), congruent with deviations of the STM height profile for the adsorbate at the upper step edge. Second, disruptions in the continuity of the halide overlayer at the steps result in significant long-range step–step interactions. Third, anisotropic metal dissolution and deposition energetics arise from phase shifts of the c(2 × 2) adlayer at orthogonal ⟨100⟩ steps. This DFT-BPNN approach offers an effective strategy for tackling large-scale surface structure challenges with atomic-level accuracy.